o
    h                     @   sv   d dl Z d dlZd dlmZmZmZ d dlmZmZ d dlm	Z	 d dl
mZmZ d dlmZ dgZG dd de	ZdS )	    N)infnanTensor)Chi2constraints)Distribution)_standard_normalbroadcast_all)_sizeStudentTc                       s   e Zd ZdZejejejdZejZdZ	e
defddZe
defddZe
defd	d
Zd fdd	Zd fdd	Ze fdedefddZdd Zdd Z  ZS )r   a  
    Creates a Student's t-distribution parameterized by degree of
    freedom :attr:`df`, mean :attr:`loc` and scale :attr:`scale`.

    Example::

        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
        >>> m = StudentT(torch.tensor([2.0]))
        >>> m.sample()  # Student's t-distributed with degrees of freedom=2
        tensor([ 0.1046])

    Args:
        df (float or Tensor): degrees of freedom
        loc (float or Tensor): mean of the distribution
        scale (float or Tensor): scale of the distribution
    )dflocscaleTreturnc                 C   s"   | j jtjd}t|| jdk< |S )Nmemory_format   )r   clonetorchcontiguous_formatr   r   selfm r   p/var/www/html/construction_image-detection-poc/venv/lib/python3.10/site-packages/torch/distributions/studentT.pymean)   s   zStudentT.meanc                 C   s   | j S N)r   )r   r   r   r   mode/   s   zStudentT.modec                 C   s~   | j jtjd}| j| j dk d| j | j dk  | j | j dk d  || j dk< t|| j dk| j dk@ < t|| j dk< |S )Nr      r   )r   r   r   r   r   powr   r   r   r   r   r   variance3   s   zStudentT.variance              ?Nc                    sB   t |||\| _| _| _t| j| _| j }t j||d d S )Nvalidate_args)	r	   r   r   r   r   _chi2sizesuper__init__)r   r   r   r   r$   batch_shape	__class__r   r   r(   ?   s   
zStudentT.__init__c                    sn   |  t|}t|}| j||_| j||_| j||_| j||_t	t|j
|dd | j|_|S )NFr#   )_get_checked_instancer   r   Sizer   expandr   r   r%   r'   r(   _validate_args)r   r)   	_instancenewr*   r   r   r.   E   s   
zStudentT.expandsample_shapec                 C   sP   |  |}t|| jj| jjd}| j|}|t|| j  }| j	| j
|  S )N)dtypedevice)_extended_shaper   r   r3   r4   r%   rsampler   rsqrtr   r   )r   r2   shapeXZYr   r   r   r6   P   s
   
zStudentT.rsamplec                 C   s   | j r| | || j | j }| j d| j   dttj  t	d| j  t	d| jd   }d| jd  t
|d | j  | S )N      ?r"   g      g       @)r/   _validate_sampler   r   logr   mathpir   lgammalog1p)r   valueyr:   r   r   r   log_prob^   s   
&zStudentT.log_probc                 C   s|   t d| j td t d| jd   }| j d| jd  t d| jd  t d| j    d| j   | S )Nr<   r   )r   rA   r   r?   r   r>   digamma)r   lbetar   r   r   entropyk   s$   "zStudentT.entropy)r!   r"   Nr   )__name__
__module____qualname____doc__r   positiverealarg_constraintssupporthas_rsamplepropertyr   r   r   r    r(   r.   r   r-   r
   r6   rE   rH   __classcell__r   r   r*   r   r      s&    )r?   r   r   r   r   torch.distributionsr   r    torch.distributions.distributionr   torch.distributions.utilsr   r	   torch.typesr
   __all__r   r   r   r   r   <module>   s   